Instructions to use Chuxia-sys/lora-tuned-planner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chuxia-sys/lora-tuned-planner with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Chuxia-sys/lora-tuned-planner")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chuxia-sys/lora-tuned-planner")
model = AutoModelForCausalLM.from_pretrained("Chuxia-sys/lora-tuned-planner", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chuxia-sys/lora-tuned-planner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chuxia-sys/lora-tuned-planner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chuxia-sys/lora-tuned-planner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chuxia-sys/lora-tuned-planner

SGLang

How to use Chuxia-sys/lora-tuned-planner with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Chuxia-sys/lora-tuned-planner" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chuxia-sys/lora-tuned-planner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Chuxia-sys/lora-tuned-planner" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chuxia-sys/lora-tuned-planner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Chuxia-sys/lora-tuned-planner with Docker Model Runner:
```
docker model run hf.co/Chuxia-sys/lora-tuned-planner
```

Uploaded model

Developed by: Chuxia-sys
License: apache-2.0
Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Lora_tuned_planner

Fine-tuned Mistral-7B for travel planning using Unsloth + LoRA.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Chuxia-sys/Lora_tuned_planner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Plan a 3-day trip to Kyoto..."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 17

Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for Chuxia-sys/lora-tuned-planner

Base model

mistralai/Mistral-7B-v0.3

Quantized

unsloth/mistral-7b-v0.3-bnb-4bit

Quantized

(193)

this model